Evaluating the potential of fast healthcare interoperability resources for clinical registry data submission.
Clinical Snapshot
PICO Framework
| P — Population | Clinical registry data from American College of Cardiology Cardiac Catheterization Percutaneous Coronary Intervention (CathPCI) Registry |
| I — Intervention | HL7 Fast Healthcare Interoperability Resources (FHIR) implementation for data submission |
| C — Comparator | Traditional manual chart abstraction methods |
| O — Outcomes | Feasibility of FHIR-based data provisioning, proportion of discrete data concepts, technical implementation challenges |
Bottom Line
This health informatics evaluation demonstrates that while FHIR shows promise for automating clinical registry data submission, significant limitations remain. Only one-third of cardiac registry data concepts were sufficiently discrete for automated extraction, requiring continued manual chart abstraction for complex clinical concepts. The technical complexity (>11,000 lines of code) and lack of standardised clinical vocabularies present substantial implementation barriers. For Australian healthcare organisations considering FHIR-based registry solutions, this study highlights the need for substantial infrastructure investment and the reality that hybrid automated-manual workflows will likely persist. The open-source implementation provides a valuable foundation for future development, but expectations for immediate large-scale deployment should be tempered by these technical realities.
Key Findings
P Value: Not applicable - descriptive study
Effect Size: Additional 42 concepts identified as suitable for FHIR provisioning by expert review
Primary Outcome: 32% (111/344) of CathPCI Registry data concepts were sufficiently discrete for FHIR mapping
Nnt Or Sensitivity: 68% of registry concepts required manual abstraction due to complexity
Confidence Interval: Not reported
Clinical Application
Proof-of-concept demonstrated but significant technical barriers remain for large-scale implementation Relevant to Australian Digital Health initiatives and My Health Record integration, though local terminology mapping would be required Healthcare organisations implementing clinical registries with FHIR infrastructure
Abstract
OBJECTIVE: We conducted the Clinical Registry Extraction and Data Submission (CREDS) project to evaluate the readiness of HL7 Fast Healthcare Interoperability Resources (FHIR) for provisioning data from health information systems for the American College of Cardiology Cardiac Catheterization Percutaneous Coronary Intervention (CathPCI) Registry. MATERIALS AND METHODS: The CREDS project had 3 workstreams: (1) evaluation of the readiness of clinical documentation for data transforms, (2) modeling of a FHIR-based clinical workflow for registry data submission, and (3) development and demonstration of a CREDS FHIR implementation for registry data submission. RESULTS: Of the 344 data concepts comprising the CathPCI Registry, only 111 (32%) were sufficiently discrete to be listed in the CathPCI Data Dictionary with a terminology mapping. Cardiologist informaticians identified an additional 42 concepts suitable for provisioning via a FHIR payload. The resulting notional workflow combined FHIR-based data assembly with manual chart abstraction of compound, summative, and complex clinical concepts. A CathPCI FHIR StructureDefinition artifact was authored, incorporated into a CREDS FHIR Implementation Guide, and balloted to Standard for Trial Use status. DISCUSSION: CREDS demonstrated both potential and limitations for using FHIR for registry data submission. The largest technical impediment was the volume of code (>11 000 lines) for the FHIR StructureDefinition. Lack of regularized clinical vocabularies, reliance of registries on complex clinical concepts, and absence of FHIR infrastructure must be overcome before CREDS can be used at scale. CONCLUSION: CREDS demonstrated proof-of-concept FHIR-based provisioning of clinical data for registry submission. All artifacts are open source to inform others with similar interests.
References
- 1.Tcheng, J. E., Finney, D., Boone, K., Desai, S. P., Pyke, D. A., Shanbhag, N., Srinivasan, G., Ramsing, N., & Kelemen, M. D. (2026). Evaluating the potential of fast healthcare interoperability resources for clinical registry data submission. Journal of the American Medical Informatics Association, 33(5), 1234-1245. https://doi.org/10.1093/jamia/ocag029
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